Nick Shea’s Representation in Cognitive Science commits him to representations in perceptual processing that are about probabilities. This commentary concerns how to adjudicate between this view and an alternative that locates the probabilities rather in the representational states’ associated “attitudes”. As background and motivation, evidence for probabilistic representations in perceptual processing is adduced, and it is shown how, on either conception, one can address a specific challenge Ned Block has raised to this evidence.

Perceptual constancy has played a significant role in philosophy of perception. It figures in debates about direct realism, color ontology, and the minimal conditions for perceptual representation. Despite this, there is no general consensus about what constancy is. I argue that an adequate account of constancy must distinguish it from three distinct phenomena: mere sensory stability through proximal change, perceptual categorization of a distal dimension, and stability through irrelevant proximal change. Standard characterizations of constancy fall short in one or more (...) of these respects. I develop an account of constancy that overcomes these problems. The account has two parts: an analysis of constancy mechanisms, and an analysis of the conditions under which a constancy capacity is exercised. I then employ this account to evaluate whether constancy is a necessary condition for perceptual representation, as some have conjectured. I argue that explanatory practice in perceptual psychology fails to support this view. Rather, it fits better with the weaker principle that representation requires specific tracking of a distal dimension. (shrink)

Recent influential accounts of temporal representation—the use of mental representations with explicit temporal contents, such as before and after relations and durations—sharply distinguish representation from mere sensitivity. A common, important picture of inter-temporal rationality is that it consists in maximizing total expected discounted utility across time. By analyzing reinforcement learning algorithms, this article shows that, given such notions of temporal representation and inter-temporal rationality, it would be possible for an agent to achieve inter-temporal rationality without temporal representation. It then explores (...) potential upshots of this result for theorizing about rationality and representation. (shrink)